induced by VCD, and a combination of HYYKF and ESCs has the advantage that they work together to promote follicles developing probably by inhibiting expression of the TGF-β1/TAK1 pathway.Alzheimer's disease (AD) is one of the most common diseases in elderly people with a high incidence of dementia at approximately 60-80%. The pathogenesis of AD was quite complicated and currently there is no unified conclusion in the academic community, so no efficiently clinical treatment is available. In recent years, with the development of traditional Chinese medicine (TCM), researchers have proposed the idea of relying on TCM to prevent and treat AD based on the characteristic of multiple targets of TCM. This study reviewed the pathological hypothesis of AD and the potential biomarkers found in the current researches. And the potential targets of berberine and evodiamine from Evodia rutaecarpa in AD were summarized and further analyzed. A compound-targets-pathway network was carried out to clarify the mechanism of action of berberine and evodiamine for AD. Furthermore, the limitations of current researches on the TCM and AD were discussed. It is hoped that this review will provide some references for development of TCM in the prevention and treatment of AD.Ulcerative colitis (UC) is a chronic nonspecific inflammatory disease of the colon and rectum with unknown etiology, and its symptoms include bloody diarrhea, abdominal pain, and hematochezia. Traditional Chinese medicine compound has a good therapeutic, multi-target effect on UC. Ganjiang decoction (GD), which is a traditional classic prescription in China, contains Zingiberis Rhizoma, Angelicae Sinensis Radix, Coptidis Rhizoma, Phellodendri Chinensis Cortex, Sanguisorbae Radix, Granati Pericarpium, and Asini Corii Colla and could be used to treat symptoms of UC. This study aimed to conduct a preliminary study before GD colon-targeted preparation, to explore the relationship between extraction method and efficacy of GD.
High-performance liquid chromatography (HPLC) was used for the fingerprinting of five preparation methods of GD. HPLC and gas chromatography were used to quantitatively analyze the important chemical components of GD and compare their differences. Mice with UC induced by dextran sulphate lts showed that the difference of the five extracts of GD in the efficacy of DSS-induced UC in mice was closely related to the extraction method.Our study improved the extraction process of GD and provided a foundation for the process of enteric-soluble preparations and a new idea for traditional Chinese medicine compound preparation.
The results showed that the difference of the five extracts of GD in the efficacy of DSS-induced UC in mice was closely related to the extraction method. Our study improved the extraction process of GD and provided a foundation for the process of enteric-soluble preparations and a new idea for traditional Chinese medicine compound preparation.In this paper, we present a parallel framework based on MPI for a large dataset to extract power spectrum features of EEG signals so as to improve the speed of brain signal processing. At present, the Welch method has been wildly used to estimate the power spectrum. However, the traditional Welch method takes a lot of time especially for the large dataset. In view of this, we added the MPI into the traditional Welch method and developed it into a reusable master-slave parallel framework. As long as the EEG data of any format are converted into the text file of a specified format, the power spectrum features can be extracted quickly by this parallel framework. In the proposed parallel framework, the EEG signals recorded by a channel are divided into N overlapping data segments. Then, the PSD of N segments are computed by some nodes in parallel. The results are collected and summarized by the master node. https://www.selleckchem.com/products/sb225002.html The final PSD results of each channel are saved in the text file, which can be read and analyzed by Microsoft Excel. This framework can be implemented not only on the clusters but also on the desktop computer. In the experiment, we deploy this framework on a desktop computer with a 4-core Intel CPU. It took only a few minutes to extract the power spectrum features from the 2.85?GB EEG dataset, seven times faster than using Python. This framework makes it easy for users, who do not have any parallel programming experience in constructing the parallel algorithms to extract the EEG power spectrum.In recent years, with the acceleration of the aging process and the aggravation of life pressure, the proportion of chronic epidemics has gradually increased. A large amount of medical data will be generated during the hospitalization of diabetics. It will have important practical significance and social value to discover potential medical laws and valuable information among medical data. In view of this, an improved deep convolutional neural network ("CNN+" for short) algorithm was proposed to predict the changes of diabetes. Firstly, the bagging integrated classification algorithm was used instead of the output layer function of the deep CNN, which can help the improved deep CNN algorithm constructed for the data set of diabetic patients and improve the accuracy of classification. In this way, the "CNN+" algorithm can take the advantages of both the deep CNN and the bagging algorithm. On the one hand, it can extract the potential features of the data set by using the powerful feature extraction ability of deep CNN. On the other hand, the bagging integrated classification algorithm can be used for feature classification, so as to improve the classification accuracy and obtain better disease prediction effect to assist doctors in diagnosis and treatment. Experimental results show that compared with the traditional convolutional neural network and other classification algorithm, the "CNN+" model can get more reliable prediction results.Characterisation of the adsorption of biomolecules, or a biocorona, on nanomaterials has proliferated in the past 10 years, as protein corona studies provide molecular level insight into mechanisms of cellular recognition, uptake, and toxicity of nanomaterials. At the crossroads of two rapidly evolving orthogonal fields, nanoscience and proteomics, the interdisciplinarity of protein corona studies creates challenges for experimental design and reporting. Here we propose a flexible checklist for experimental design and reporting guidelines to outline Minimum Information about Nanomaterial Biocorona Experiments (MINBE). The checklist for experimental design, compiled after review of reporting within the protein corona literature, provides researchers with prompts to ensure best practice experimental approaches for each stage of the workflow, collated from the nanoscience, proteomics, and bioinformatics fields. Reporting guidelines are also assembled from established sources, integrated to span the entire workflow and extended and modified to aid interdisciplinary researchers in the most challenging stages of the workflow.